Representation of Modes of Variability in Six U.S. Climate Models

dc.contributor.authorOrbe, Clara
dc.contributor.authorRoekel, Luke Van
dc.contributor.authorAdames, Ángel F.
dc.contributor.authorDezfuli, Amin
dc.contributor.authorFasullo, John
dc.contributor.authorGleckler, Peter J.
dc.contributor.authorLee, Jiwoo
dc.contributor.authorLi, Wei
dc.contributor.authorNazarenko, Larissa
dc.contributor.authorSchmidt, Gavin A.
dc.contributor.authorSperber, Kenneth R.
dc.contributor.authorZhao, Ming
dc.date.accessioned2025-10-22T19:58:28Z
dc.date.issued2020-08-03
dc.description.abstractWe compare the performance of several modes of variability across six U.S. climate modeling groups, with a focus on identifying robust improvements in recent models [including those participating in phase 6 of the Coupled Model Intercomparison Project (CMIP)] compared to previous versions. In particular, we examine the representation of the Madden–Julian oscillation (MJO), El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), the quasi-biennial oscillation (QBO) in the tropical stratosphere, and the dominant modes of extratropical variability, including the southern annular mode (SAM), the northern annular mode (NAM) [and the closely related North Atlantic Oscillation (NAO)], and the Pacific–North American pattern (PNA). Where feasible, we explore the processes driving these improvements through the use of “intermediary” experiments that utilize model versions between CMIP3/5 and CMIP6 as well as targeted sensitivity experiments in which individual modeling parameters are altered. We find clear and systematic improvements in the MJO and QBO and in the teleconnection patterns associated with the PDO and ENSO. Some gains arise from better process representation, while others (e.g., the QBO) from higher resolution that allows for a greater range of interactions. Our results demonstrate that the incremental development processes in multiple climate model groups lead to more realistic simulations over time.
dc.description.sponsorshipThis work was funded fromNASA MAP, DOE, and NOAA, and arises from the 2019 U.S. ClimateModeling Summit inWashington,D.C., cochaired by Steven Pawson and Gavin Schmidt. Climate modeling at GISS and the GMAO is supported by the NASA Modeling, Analysis and Prediction program, and resources supporting this work were provided by the NASA High-EndComputing (HEC) Program through theNASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center. This research was also supported as part of the Energy Exascale Earth SystemModel (E3SM) project and Regional and Global Climate Modeling Program, both funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. Work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under ContractDE-AC52-07NA27344. All presented material relevant to the CESM project, which is supported primarily by the National Science Foundation (NSF), is based upon work supported by the National Center for Atmospheric Research, which is major facility sponsored by the NSF under Cooperative Agreement 1852977. Portions of this study were supported by the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the U.S. Department of Energy’s Office of Biological and Environmental Research (BER) Cooperative Agreement DE-FC02-97ER62402. Computing and data storage resources, including the Cheyenne supercomputer (doi:10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. The GEFS SubX forecasts were partially supported through NWS OSTI and NOAA’s Climate Program Office (CPO) Modeling, Analysis, Predictions, and Projections (MAPP) program. We also thank the GFDL model development team, the leadership of NOAA/GFDL for their efforts and support in developing CM4/ESM4 as well as the many GFDL scientists and engineers who conducted the CM4/ESM4 runs and made the data available at ESGF. We acknowledge the modeling groups, PCMDI and the WCRP’s Working Group on Coupled Modelling(WGCM) for their roles in making available the CMIP multimodel datasets. Support of this dataset is provided by the Office of Science, U.S. Department of Energy. We thank the three reviewers whose comments helped greatly increase the clarity of the manuscript.
dc.description.urihttps://journals.ametsoc.org/view/journals/clim/33/17/jcliD190956.xml
dc.format.extent27 pages
dc.genrejournal articles
dc.identifierdoi:10.13016/m2qic7-nh9t
dc.identifier.citationOrbe, Clara, Luke Van Roekel, Ángel F. Adames, et al. "Representation of Modes of Variability in Six U.S. Climate Models". Journal of Climate. 33, no. 17 (2020): 7591–617. https://doi.org/10.1175/JCLI-D-19-0956.1.
dc.identifier.urihttps://doi.org/10.1175/JCLI-D-19-0956.1
dc.identifier.urihttp://hdl.handle.net/11603/40586
dc.language.isoen
dc.publisherAMS
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC GESTAR II
dc.rightsThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
dc.rightsPublic Domain
dc.rights.urihttps://creativecommons.org/publicdomain/mark/1.0/
dc.subjectModel evaluation/performance
dc.subjectInterannual variability
dc.subjectClimate models
dc.titleRepresentation of Modes of Variability in Six U.S. Climate Models
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0003-3274-8542

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